| A new class of stochastic EM algorithms. Escaping local maxima and handling intractable sampling S Allassonnière, J Chevallier Computational Statistics & Data Analysis 159, 107159, 2021 | 33* | 2021 |
| Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data J Chevallier, S Oudard, S Allassonnière Advances in Neural Information Processing Systems, 1152-1160, 2017 | 22* | 2017 |
| A coherent framework for learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data J Chevallier, V Debavelaere, S Allassonniere SIAM Journal on Imaging Sciences 14 (1), 349-388, 2021 | 5 | 2021 |
| Pandemic intensity estimation from Stochastic Approximation-based algorithms P Abry, J Chevallier, G Fort, B Pascal 2023 IEEE 9th International Workshop on Computational Advances in Multi …, 2023 | 1 | 2023 |
| Hierarchical Bayesian Estimation of COVID-19 Reproduction Number P Abry, J Chevallier, G Fort, B Pascal | | 2024 |
| Minimax density estimation in the adversarial framework under local differential privacy M Albert, J Chevallier, B Laurent, O Sacko arXiv preprint arXiv:2403.18357, 2024 | | 2024 |
| Statistical models and stochastic algorithms for the analysis of longitudinal Riemanian manifold valued data with multiple dynamic J Chevallier Université Paris Saclay (COmUE), 2019 | | 2019 |
| Algorithme SAEM: Par dela l’étape de simulation J Chevallier | | |